Career Analytics Platform

ABSTRACT

A method and system for assessing a career profile of a candidate is disclosed. The system comprises a database configured to include a plurality of parameters and a plurality of scores respective to each parameter, a parser configured to parse the career profile to identify at least one parameter from the plurality of parameters within the career profile and an analytics engine configured to retrieve a score from the plurality of scores for the at least one parameter identified within the career profile, compute a score of at least one category based on the retrieved score, wherein the at least one category comprises the at least one parameter identified within the career profile, and provide feedback to the candidate on the career profile in accordance with the computed score of the at least one category.

TECHNICAL FIELD

The present invention relates generally to document analysis; and morespecifically, to methods and systems for leveraging a usersprofile/resume/cv to perform smart data analytics to help variousstakeholders in making better human capital decisions.

BACKGROUND

Current methods of helping users in their career journey rely heavily onhuman intervention, whether it is in the form of a career coach, resumewriter, own personal network, etc. There are no platforms that leveragedata analytics to give the consumer objective guidance on a) what theyneed in light of their career goals b) what their career goals should bebased on their own unique profile. The invention aims to provide a dataanalytics based system that helps candidates make better decisions abouttheir careers regardless of the career they are in.

On the companies side, there are limited methods that attempt to removebias in the recruitment process, while capturing the unique requirementseach company inherently has for a job role. In today's world ApplicationTracking Systems and other mechanisms simply use a set of keywords tofilter through candidates, creating a very binary phenomenon ofcandidate selection. Whereas candidate selection is inherently aspectrum some candidates are a better fit for some jobs than others. Theinvention aims to build an automated system/mechanism to objectively,effectively and efficiently simplify the recruiting process while takingcare of the inherent customizations and complexity in it. It emulatesthe behaviour and assessment of human mind works.

SUMMARY

In an embodiment, a method for assessing a career profile of a candidateis disclosed. The method comprising: parsing the career profile toidentify at least one parameter from a plurality of parameters withinthe career profile; wherein the plurality of parameters are defined in adatabase and a plurality of scores are associated with each parameter;retrieving a score from the plurality of scores for the at least oneparameter identified within the career profile; computing score of atleast one category based on the retrieved score, wherein the at leastone category comprises the at least one parameter identified within thecareer profile; and providing feedback to the candidate on the careerprofile in accordance with the computed score of the at least onecategory.

The invention aims to simulate the way the recruiter behaves whenassessing information presented by a potential candidate to providebenefits to two main types of audiences.

Firstly, it demystifies for career professional how exactly they shouldrepresent themselves to recruiters to showcase their relevant skills andexperiences. It helps candidates identify potential jobs for which theyare a good fit, and how to chart their career journey to get to theirgoals.

Secondly, for the recruiter it provides a mechanism to score candidatesand shortlist them removing many inefficiencies in the process to selectcandidates that are a fit for them based on their specific criteria nota broad job description.

The invention aims to help career professionals get objective careerguidance based on data and analytics about how good their resume is bothin general as well as specific to particular careers they are interestedin.

It further helps them to leverage data analytics, predictive analyticsto benchmark their resumes against their peer group to truly see wherethe quality of their skills and content on their resume is. Thefundamental thought here is that career professionals don't get targetedand objective guidance based on data in todays' environment. Mostinnovation is targeted at helping companies filter out using basiccriteria and keywords.

The invention aims to truly understand the context of the career of aprofessionals profile and accomplishments as listed on their resume, torate their skills and competencies and give them a view of where theystand in general and also relative to others in similar positions.

Moving deeper into the challenges of the career professional, theinvention leverages data analysis, predictive analytics and data miningto identify potential career paths that they may take to answer thequestion of what career paths they could pursue both in the short-termand longer term. Further, it aims to provide clarity on what are thesteps the career professional can take to get to an objective, and alsohow likely it is for them to actually get there based on how others likethem have done in the past.

On the recruiter side, the invention aims to move away from complex Jobdescriptions to simplify the method of filtering by providing a methodto assess candidates skills, career trajectory and other elements toidentify whether they will be a fit for the job or not.

Here the invention gets into the relative prioritization of therequirements that a recruiter has which is not reflected in anydocument, but rather is sitting in their mind. Using this the inventioncreates a customized algorithm for each job that the recruiter has toensure that they are able to identify candidates who are the best fitfor that. In addition by analyzing their network and other employeeprofiles in the company, the invention applies criteria beyond skillassessment to analyze the likelihood of a candidate making it throughthe recruiting process. It leverages predictive analytics in algorithmsto predict where candidates fit both in the short and longer term.

This makes the invention highly relevant for any company conductingrecruiting.

The invention can be used to reduce the cost of hiring by automating thefirst step of resume filtering, more efficiently transferring candidatesthrough the process that are likely to succeed in other stages ofrecruiting.

The invention also helps job seekers identify who in their network canrefer them to positions for which they have the highest likelihood ofsuccess further improving the chances of success both in the recruitingprocess as well as during their career journey at the company.

By matching open opportunities to their resume assessment, leveragingpredictive analytics about both the candidates and other careerprofessional career path/journey and identifying network members who arelikely targets for mentoring, referring and support the applicationincreases the likelihood of success for a career professional as well asa recruiter.

Every professional and job seeker is well versed with the concept of acareer coach. A Career Coach possesses experience having dealt with avariety of other students and professionals, know-how to read from aprofile/resume, understand personality issues and individual preferencesto create a career roadmap including developmental interventions. Usingintelligent algorithms, predictive models, context analysis usingmachine learning and natural language processing,

VMock has built a SMART CAREER COACH that can help millions of careerprofessionals and students assess and improve their resume/profiles;align with targeted opportunities with optimum/best match and calibratetheir career decisions with Predictive Models based on crowdsourcing ofcareer paths. This SMART CAREER COACH does not stop just at that i.e.getting you an entry into a company, but also suggests how to succeed inthe role, who to target as mentor and continues to help for the nextcareer move.

Another manifestation of the invention is a business model innovationallowing for targeting and lead generation for variety of servicesincluding head hunting, resume writing, interview preparation, etc.

Embodiments of the present invention substantially eliminate or at leastpartially address the aforementioned problems in the prior art, andenable efficiently management of the usage data corresponding to theplurality of tracking identifiers of the web property.

Additional aspects, advantages, features and objects of the presentinvention would be made apparent from the drawings and the detaileddescription of the illustrative embodiments construed in conjunctionwith the appended claims that follow.

It will be appreciated that features of the present invention aresusceptible to being combined in various combinations without departingfrom the scope of the present invention as defined by the appendedclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The summary above, as well as the following detailed description ofillustrative embodiments, is better understood when read in conjunctionwith the appended drawings. For the purpose of illustrating the presentinvention, exemplary constructions of the invention are shown in thedrawings. However, the present invention is not limited to specificmethods and instrumentalities disclosed herein. Moreover, those skilledin the art will understand that the drawings are not to scale. Whereverpossible, like elements have been indicated by identical numbers.

Embodiments of the present invention will now be described, by way ofexample only, with reference to the following diagrams wherein:

FIG. 1 is a schematic illustration of a career analytics platform inaccordance with an embodiment of the present invention;

FIG. 2 is an illustration of elements or data sources of the system inaccordance with an embodiment of the present invention;

FIG. 3 is an illustration of machine learning elements of the system inaccordance with an embodiment of the present invention;

FIG. 4 is an illustration of a career coach in accordance with anembodiment of the present invention;

FIG. 5 is an illustration of a method for generating structure data froma career profile of a candidate in accordance with an embodiment of thepresent invention;

FIG. 6 is an illustration of a method for scoring and improving theresume in accordance with an embodiment of the present invention;

FIG. 7 is an illustration of a method for editing the resume inaccordance with an embodiment of the present invention;

FIG. 8 is an illustration of a method for providing feedback from anetwork of the candidate in accordance with an embodiment of the presentinvention;

FIG. 9 is an illustration of a method for providing feedback to thecandidate in accordance with an embodiment of the present invention;

FIG. 10 is an illustration of a method for identifying career path forthe candidate in accordance with an embodiment of the present invention;

FIG. 11 is an illustration of a method for exploring a career path inaccordance with an embodiment of the present invention;

FIG. 12 is an illustration of a method for synchronizing differentprofiles of the candidates in accordance with an embodiment of thepresent invention; and

FIG. 13 is an illustration of a method for scoring the candidate by arecruiter in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

The following detailed description illustrates embodiments of thepresent invention and ways in which they can be implemented. Althoughsome modes of carrying out the present invention have been disclosed,those skilled in the art would recognize that other embodiments forcarrying out or practicing the present invention are also possible.

In this document there are two kinds of profiles—private user profileand a public user profile. A private user profile refers to a documentlike a resume/cv/objective document etc. A public profile is an opendocument where the user reflects his or her career profile. An exampleof this would be a linkedin profile. Wherever in this document a resumeis referred/called out it is referring to all possible forms of aprivate profile. Wherever in this document a linkedin profile isreferred/called out it is referring to all possible forms of a publicprofile.

Referring to FIG. 5, the user uses the Resume Improvement module toenter their resume into the system, receive an objective score,benchmark themselves against others, identify areas of improvement andview detailed feedback on both the structure of their resume as well asthe actual content within the text of the Resume/Text Profile.

Upon viewing the feedback, the user then can incorporate suggestionsinto their Resume, and re-score their resume to get a new Resume Score.They can repeat this process unlimited times until they are satisfiedwith their score and feedback on their resume.

Entering of information into the Resume Improvement module: the user canupload their resume to the system, or create a profile on the platformitself where they represent their education, skills, experiences,achievements, hobbies and other pieces of information they wish torepresent in their resume or profile.

Structuring/Parsing of data: If the user chooses to upload theirexisting resume into the platform, the system parses the text in theirresume to identify resume sections, bullets, context of skills, keywordsand phrases, and stores data into a structured format.

Storing of user data in structured data in database: All elements of ausers data available are stored in a structured format.

Resume Scoring, Feedback and Improvement

As illustrated in FIG. 6, each resume receives a score based on acustomized algorithm. The relevant algorithm is selected based on theusers experience level, job role and tier of company. Structured dataextracted from resume is analyzed using natural language processing,part of speech, and matching to existing data sets of soft andfunctional skills, other career professionals career profiles.

The benchmarking module provides inputs into the scoring in setting thebenchmarks incorporated in the score on a dynamic basis.

Summary level statistics/analysis of other users profiles are alsoshared with the user. These include any analysis possible on usersprofile including but not limited to:

-   -   1. Score improvements of others    -   2. Keyword clouds of other users    -   3. Career background averages and statistics    -   4. Skills profiles of other users    -   5. Comparison across parameters including sections, number of        words, skills, number of bullets, keywords

Total Resume score is calculated based on presentation score+impactscore+skills score, which is then normalized and put on a 0 to 100 scaleto imply %.

Presentation module score is based on the following:

-   -   1. Margins—margins in resume are matched against best case        margins pulled from database of user data(rm)    -   2. Section layout—sections in the resume are broken down and        margins and layout is analyzed and matched with values stored        database (rsl)    -   3. Sections in resume—the sections in resume are matched against        standards both for number and which sections. Based on profile        certain sections are mandatory for example for students in        engineering education, GPA, extracurricular, internships would        be mandatory sections. The sections which are mandatory, are        derived from benchmarks from other similar user groups. Number        of missing sections are stored in field for scoring(rms). Users        are penalized for having too many sections and too many pages.    -   4. Formatting—formatting of resume is analyzed to ensure        alignment of all resume elements, bullets, and other content on        the resume. (rf)    -   5. Spell check—spelling errors are identified, # of mistakes are        used as parameter in scoring (rse)    -   6. Prioritization of content—most relevant for content should        showcase on the top of relevant section.

Formula for Presentationscore=wp(w1*α1*rm+w2*α2*rs1+w3*α3*rms+w4*α4*rf+w5*α5*rse)

w1 . . . 5 is pre-set with w5 being lowest weight and other elementsweighted equally.

α1 . . . 5 is the benchmark derived from benchmarking similar userresumes across the exact parameter for example for #of sections it wouldbe # of sections similar users have in their resumes.

wp refers to the weight presentation score has in overall resume score.The weight also varies based on user group—student and job seeker level

All values are normalized on a scale of 0 to 1. 1 being maximum score,and 0 being lowest score

Impact module score is a function of the following elements:

-   -   1. Career progression score    -   2. Bullet scores    -   3. Achievements score

Formula for career progression—career path nodes are matched to nodes ofresumes with high scores; Level of match is normalized with 1 beingexact match, and 0 being no match.

Bullet scores: Based on the section the following parameters are used.Not all parameters apply to all sections, for example education sectionis not strict about quantification, and starting of bullets withaction-oriented text.

-   -   a. Action-oriented—description of the actions candidate took in        active form vs. passive form. A library of action-oriented terms        is the basis for this. Strong action verbs have higher weights,        and weak action verbs have lower weights.    -   b. Quantification—How well the candidate has quantified the        impact of their actions as well as quantification of elements in        the experience that will help someone understand the impact of        work. This is both qualitative and quantitative values.    -   c. Passive usage—use of passive language in description of        experience (acts as a negative parameter)    -   d. Size of bullet—how concise and descriptive the experience is.    -   e. Responsibility driven text—usage of language that describes        what responsibilities were as opposed to outcome of work (acts        as a negative parameter)

The optimum value for all of these parameters is set by benchmarking.When the user gets optimum value and >optimum value, they get anormalized score of 1. Each of the parameters above is also assigned aweight based on importance of parameter for user profile, like forexample for students quantification weight may be lower than for a salesprofessional. Again benchmarking of similar user profiles is done todetermine what level is appropriate for each group.

Score of each bullet is calculated using a combination of aboveparameter, all bullet scores are added and averaged to get bullet scoreof user.

Bullet score=(b1+b2+b3 . . . bn)/n where b1 is score of bullet 1 and soon.

Included in the impact score is also an assessment of achievements thatuser has stated on their resume that are part of educational, and othersections. Another dimension in assessment is consistency ofachievements.

Achievements are broken down into academic, and non-academicachievements such as GPA, Honors, Awards, Scholarships, Organizationmemberships and other areas mentioned in a users resume. (ra)

Achievement score=w1*ra1+w2*ra2 . . . +wn*ran

Weights vary based on importance of a particular achievement given theuser profile and background, and goals.

Total Impact score=wcp*cpscore+wi*iscore+wa*ascore

Where wcp=weight of career progression, wi=weight of impact, wa=weightof achievement

Total impact score itself is then normalized and used in computation oftotal resume score

Skills Score is based on combination of soft (behavioral) skills, andfunction, industry specific skills

Based on a users profile, the set of soft skills, functional andindustry relevant skills are chosen which are most important. These arepulled from the database of job roles/profiles. For instance a studentwould mainly focus on 5 core soft skills as communication,problem-solving, teamwork, leadership and initiative and some skillsrelevant to the degree they are pursuing. Where as a job seeker in thehealthcare industry would be rated on soft skills, functional andindustry relevant skills that correspond to their current profile.

To obtain the skills score the following is done:

To provide a score on skills the analytics engine takes all data storedin the users profile and identifies what job role/function/industry theuser fits into for job seekers, and for students—degree discipline,desired function/company.

-   -   1. This data is then matched with skills matching database that        contains keywords, phrases, job roles, patterns that determine        whether someone has a skill or not.    -   2. For each skill—a count of matches is generated with weights        for each match is calculated    -   3. Count of matches and weight is added up and matched to        benchmarks for that “type” of profile. The score is then        normalized on a 0 to 1 scale. Benchmarks determine whether the        user is high, medium or low on a skill    -   4. This is repeated for all skills stored in the database for        that particular profile of user.

skill score=sum (all keywords weights)—normalized on a 0 to 1 scalewhere 1 is highest level competency for that skill and 0 is not havingthat skill. This is repeated for all skill

Total skills score=w1*α1*s1+w2*α2*s2+ . . . +wn*αn*sn

Another element of the score is whether the candidate has gaps in therecareer—this could potentially be a negative factor in the score.

As illustrated in FIG. 6, the user is provided with their Resume Scorethat is benchmarked dynamically and plotted on a curve, to show wherethey stand amongst the relevant peer group. The process of plottingtheir resume is as follows:

-   -   1. A dynamic benchmarking curve is created based on scores of a        target peer group, the peer group can be customized, and is        broad or specific as desired by the user or community. It can be        a plot of all users in the system, only those within the same        role as user, or all students with similar backgrounds, or only        employees within that company in that job role, etc. All        possible parameters of user profile including education degree,        college, job role, experience, company name, can be applied as        filters to determine the relevant user cluster    -   2. The user is then plotted on this normal distribution curve        and illustrated exactly where the user stands, based on        benchmarking the user is told whether they have a poor score, ok        score, or a great score relative to that peer group.

Referring to FIG. 6, the user can view the feedback on their resume onall of the areas—presentation, impact, and skills in an aggregate viewas well as a detailed view.

In the aggregate view they can see exactly where their resume stands ineach area. This information is represented is an visual manner withsamples for all areas, where the platform gives the user customizedsamples to see how they can do better.

The samples are pulled from the samples library matched to the usersprofile to ensure they are relevant for the user using tags already inthe system.

To view detailed level feedback, the user gets into the bullet-viewwhere they are provided with detailed feedback on all relevantparameters based on which section of the resume the bullet is in.

-   -   1. Feedback on all parameters is provided displaying the logic        for scoring a bullet high, medium or low.    -   2. For each bullet customized samples are found in the samples        library using the following logic:        -   a. Parse each bullet using language processing to identify            action, primary objects and other elements.        -   b. Match keywords and phrases against those in samples        -   c. Use encoded priority to identify which phrases, keywords,            word patterns to prioritize during matching        -   d. Score bullets' found based on matching criteria and            showcase sample bullets with highest match score to user        -   The following formula is used to determine the bullet match            score:

Bullet match score=Sum(Score of keywords matched)+Sum(Score of phrasesmatched)

-   -   -   Normalize on scale of 0 to 1.

    -   3. The user views the feedback on his/her bullets along with the        customized samples

As illustrated in FIG. 8, after viewing feedback the user enters editmode online or offline to make suggested changes to his/her resume. Inedit mode the user does the following:

-   -   a. Makes changes to resume elements/bullets    -   b. Re-runs scoring of resume or just bullet    -   c. Reviews feedback again    -   d. Repeats this process until user is satisfied

The user follows a process of dynamic score based Score Improvement oftheir profile where they follow a process leveraging feedback from thesystem to dynamically score their improvements and also see the scoreimprove dynamically.

A higher resume score increases likelihood of getting an interview.

Referring to FIG. 9, the user can use the platform to request forfeedback virtually on their resume from an external entity, this couldbe someone that is in their professional network, that is a career coachin college, friend, or anyone they are connected to.

The platform allows users to port members of their email, socialnetworks (like Facebook, linkedin, etc), or simply entering an emailaddress of the intended recipient.

Based on the career objective of the user, the platform recommends whoto send the request to. Network members are scored based on how relevantthey are to the users. Members who are in similar desired roles, orcompanies; or HR managers within desired companies/industries/functionsare also prioritized above those that do not match any of thesecriteria. The user however can bypass any of these recommendations andselect whomsoever they want to send the request to.

Networkscore=pastjobrolematch+pastfunctionmatchpastjobrolefunctionindustrymatch+desiredcompanymatch+w*HRw*generalsmartsmatch

Pastjobrolematch=1−if exact match, similar job roles are 0.8 match, andno match is given 0.

Similar logic is done for pastfunction and other matches, whereharmonized data sets on job roles, company, function are leveraged toassess the “closeness” of a job role, function, industry, company,college on a normalized scale.

The system scores network members and presents those with highestscores, the user selects on of these members or decides to send towhomsoever they wish to do so.

In addition the user selects what areas of the resume they want feedbackfrom their network member. This can be at:

-   -   1. Bullet level    -   2. Section level    -   3. Highlighted portion level    -   4. Entire resume itself

An email with a link to the users resume is sent to their networkmember.

The network member clicks on the link and is taken to the platform wherethey can start the process of giving feedback to the user

The Feedback provider gives feedback to the candidate on the followingdimensions on the areas that the user had selected:

-   -   a. Bullet level—for each bullet, both structure and content;        standard rating of bullet    -   b. Presentation—overall presentation    -   c. Communication—overall communication of resume elements    -   d. Rating for content and communication    -   e. Overall rating/scoring of resume—Feedback providers score of        resume    -   f. Feedback on next career step for candidate    -   g. Inputs on other parameters of candidates career including but        not limited to education, roles, open opportunities, companies,        training programs, people to network and talk to and others.

Once the feedback provider is done giving feedback to the user, they cansubmit their feedback.

Upon submitting their feedback, the candidate/user of the platform cannow view the feedback that was given by their network member asillustrated in FIG. 1.5

The user can also compare and contrast feedback on each element and seeaverage scores across feedback providers, as well as see patterns acrossfeedback providers. The system analyzes all the data, identifies themesand presents these back to the user to showcase:

-   -   1. Main areas needing improvement    -   2. Areas where the user is doing a good job    -   3. Inconsistencies across feedback providers    -   4. Heatmap of feedback showcased on the profile

The user can incorporate feedback by following a similar edit processand re-score their resume to see feedback on it.

Career Fit

Referring to FIGS. 10A & 10B, the career fit module enables the user toidentify which career paths are the best fit for him based on matcheswith others who have entered those career paths. As an output, the useris provided with scores for top job roles that are a fit for him.

In an embodiment, the method for computing a jobfit, score is disclosed.The method identifies a cluster of career profiles within the databasewhich includes parameters and/or categories indicating at least onecareer path for the candidate. The method further determines presence ofat least one skill for the candidate within the cluster. Further, themethod applies at least one benchmarking rule to determine score of theat least one skill of the candidate and computes the jobfit score forthe candidate based on the score of the at least one skill of thecandidate.

Structured data about the user is obtained from their resume/onlineprofile which is stored in the user database. The data in the databaseis matched against jobs and other user profiles to create jobfit scorefor each job. The jobfitscore may also be referred to as careerfitscorein the description.

-   -   1. The jobfit score is calculated as follows:    -   2. Jobfit score=career progression match+soft skills        match+functional skills match    -   3. Career progression match=we*education degree        match+wct*collegetier+wjr1*job role 1 match_ . . .        +wjrn*jobrolenmatch    -   4. Where we=weight given to education,        wet=weightgiventocollegetier, wjr=weight given to job role    -   5. Soft skills match=skillscore1+skillscore2+ . . .        +skillsscoren    -   6. Where        skillscorei=countkeywordsmatched+countphrasesmatched+countpatternmatched    -   7. Skill is pulled from the table in database where job role,        and match attributes are stored. The data is normalized on a 0        to 1 scale with 1 being tightest match and 0 being no match. The        scale is non-linear.    -   8. Similar formula for functional skills match. A skills map and        skills timeline is created for each user and matched to        calculate the skills match for all types of skills.    -   9. Refer to section xyz for structure of job role matching        database.    -   10. After calculating of scores, the scores are rank ordered and        job roles with highest scores are selected

Top job roles matching for a particular user are showcased visuallyalong with the score for each role that matches in order of priority

Bell-curves of skills from the user database can serve as training dataor entire set of user profile is used as training set, skill patternsand also profile vectors are used to create relevant user clusters.Users closeness to other user profiles is calculated and based on matchagainst elements of profile vector to determine match.

Elements of profile vector include all elements of users profile alongwith skills assessment, and assessment of other parameters including butnot limited to job roles, education degree, education tier, companyprofile, company tier, experience timelines, skills timelines andladders, along with other inputs.

Job opportunities available are crawled from the web and matched to theusers top job roles, matching not just the job role, but the function,industry as well as tier of company that would fit the users background.

In addition, the method identifies at least one job opportunity based onthe job fit score of the candidate and at least one professional from anetwork of the candidate for the at least one job opportunity using anetwork score. The method computes a jobfitatcompany score for the atleast one job opportunity based on the jobfit score and the networkscore.

In an embodiment, the users professional network is analyzed and openopportunities where user has professional network members are scoredhigher increasing the jobfit score for that particular role. In anexample, the network score is dependent on a group of members consistingat least one of the of members in function, members in industry, membersin function in industry, members seniority in function in industry,members in HR role in industry, members in company, members in companyin function, members in company in HR, and members in similar tier othercompanies in function

The jobfitatcompany score=w1*jobfit score(specific toopportunity)+w2*networkscore where networkscore=#ofmembersinfunction+#membersinindustry+#ofmembersinfunctioninindustry+#ofmembers,seniorinfunctioninindustry+#membersinHRroleinidustry+#ofmembersincompany+#ofmembersincompanyinfunction+#ofmembersincompanyinHR+#membersinsimilartiercompanyinfunction

The score similar to all scores is normalized to obtain the networkscore.

The principle idea behind this is that the user is more likely to find ajob if their network is likely to be close to the opportunity as asignificant % of jobs are found through ones network.

Opportunities with the highest jobfitatcompanyscore are shown to theuser.

Career Explorer

In an embodiment, a method for exploring a career path is disclosed. Themethod includes receiving an input from the candidate regardingselection of at least one career path to be achieved within a timeframeand recommending the candidate at least one action to pursue the careerin the at least one career path.

As illustrated in FIG. 11, another manifestation of the career fitmodule is when the user is allowed to select and explore career paths.This module in the application allows the user to select a career goal,and identify what are the gaps in his/her career and how likely he is tobe able to achieve that career goal. Career goal can be a particular jobrole in general, or a particular job role within a company, or aparticular job role in an industry. In addition, the user can input atime dimension to the goal, which can be short term (1-2 yrs), mediumterm (3-5 yrs), and long-term (5+ years).

The user selects careers interested in and defines time-frame againstthose. CareerFitscore is calculated for all careers chosen by the user.

The score is calculated as follows:

-   -   1. Identify user profiles from database in selected career    -   2. Match users career trajectory to those in career. If perfect        matches in time-frame, with most frequent paths give 1 on        normalized score, if no match found give score of 0.    -   3. CareerFitScore=CareerProgressionmatch (refer to 0081) in        specified time frame. For short-time frame look at match with        what other users in similar career were doing 1-2 years ago, and        same for other time-frames

In the next steps gaps in skills, trajectory, education, etc that usershould target to achieve career paths are illustrated. Gaps areshowcased on the following dimensions:

-   -   Skills/Competency levels    -   Educational background    -   Career path info

The user is able to visually see the gaps in levels of skill

To help the user get career guidance also to understand how to targetcareer tracks, the system analyzes their extended network and the systemuser database, showcase potential mentors/coaches/experts to target forcareer guidance.

The user sends a request via the system for guidance and take theconversation offline or continues it on the platform leveragingcommunication tools provided.

Resume Synching with Social Network Profile (e.g. LinkedIn)

In an embodiment, a method for identifying synchronization between twoprofiles of the candidate is disclosed. The method includes receivinganother career profile of the candidate, determining synchronizationbetween the career profiles of the candidate via matching at least oneof the parameters and categories between the career profiles andindicating to the candidate an extent of synchronization between thecareer profiles of the candidate.

In addition, the method includes computing job score for at least onejob for the career profiles of the candidate and determining a job synchscore for the at least one job for the career profiles of the candidate.

Referring to FIG. 12, a manifestation of the application is ensuringthat a users resume and linkedin profile as consistent and presentingthe same information to employers. The application can be used tohighlight what compare and contrast the resume and linkedin profile.While this process is highlighting what the application would do inrelation to a resume and linkedin profile, needless to say that itdoesn't have to be only a linkedin profile, any social network profileshowcasing the users career information can be used here.

As a first step the user imports their linkedin profile, data from thelinkedin profile is structured into the same fields in the database tofacilitate matching and reconciliation.

The profile is analyzed, and career path, soft and functional skills andcompetencies are identified in exactly the same manner as in the ResumeScoring and CareerFit processes.

The following elements are matched:

-   -   1. Career Path from both the resume and linkedin profile are        created and matched to see if they are consistent. Data of both        paths is stored and gaps in either are identified    -   2. Skills Analysis—data from resume and linkedin profile (all        text) is analyzed, frequency is calculated for each skill and a        competency rating is assigned (similar to skills scoring process        as defined above). This is done for all soft skills and        functional skills found in both the resume and linkedin profile.    -   3. Educational, additional and other sections data—data from all        other sections is also matched to ensure that there is        consistency in naming as well as content.    -   4. Keywords and phrases, patterns—frequencies of keywords,        phrases and other text patterns are analyzed in both and used in        displaying the distribution of the same to the user

After analysis, the data is represented back to the user to showcase thefollowing:

-   -   1. Areas where skills/competency ratings same in both    -   2. Areas where skills/competency ratings different    -   3. Difference in keywords, phrases, etc used in both    -   4. Analysis of what resume is projecting as users career        interest, vs. what linkedin is showing as career interest, based        on careerfit match of both    -   5. Career path gaps and mismatches    -   6. Resume strengths vs. Linkedin strengths

Leveraging predictive analytics the system can showcase aspirationalprofiles to the candidate to showcase where their career path could go.Public profile should be optimized around aspirational goals. Candidatesmay have multiple aspirations so the profile should be optimized arounda portfolio of aspirations.

Synch score can be calculated for any job and will vary across jobroles.

Candidate Assessment/Scoring

In an embodiment, a method for customizing the jobfitscore is disclosed.The method includes receiving an input from a recruiter regarding atleast one job; wherein the input comprises at least one of a jobdescription, at least one parameter corresponding to the job descriptionand at least one weight for the at least one parameter, accessing careerprofiles of a plurality of candidates in a resume database anddetermining jobfit score of each career profile in accordance with theinput of the recruiter.

Referring to FIG. 13, another manifestation of the innovation is thatcompanies/recruiters can use elements of CareerFitScore and ResumeScoreto filter out relevant candidates and calculate the CandidateScore forall candidates.

The recruiter can either score/filter existing user database residing inapplication or enter a set of resumes, run them through candidatescoring module or combine resumes in the system and those that he/sheuploads from,his/her own recruiting platform.

Candidate scoring is done in one of two ways as illustrated in FIG. 5 orcombination of both.

Option 1. The recruiter decides to use platform job roles to identifyhigh scoring candidates. This is done by leveraging existing CareerFitalgorithm for that job role. Once CareerFit scores are calculated acrossall resumes, they are benchmarked against the entire set, and the scoresare plotted on a normal distribution and optimized as follows:

-   -   Set highest level score benchmark as highest scoring        candidates/resumes removing outliers    -   Normalize all scores relative to new benchmark set by highest        scoring resumes where that resume would get 1 and the bottom        most would be close to 0.    -   Plot all CustomizedCareerFit Scores on normalized scale as new        distribution of CandidateScores

Option 2. The recruiter decides to enter specific criteria and relativepriorities across criteria selected by Recruiter to calculateCustomCandidateScore for each candidate. The recruiter can also load ajob description into the system and get matches using the belowcriteria.

The following is the structure for entering criteria.

Education Parameters

-   -   1. Education Degree—specific degree e.g. BSc or MBA in Finance;        Ranking/Importance (High, Medium, Low, None)    -   2. College name—specific name; Ranking/Importance (high, medium,        low, none)    -   3. College Tier—Tier 1,2,3; Ranking/Importance (high, medium,        low, none)    -   4. GPA—Ranking/Importance (High, Medium, Low,None)

Job Role—Parameters (Across Past 3 Experiences)

-   -   1. Job role—specific role e.g financial analyst, etc;        Ranking/Importance    -   2. 2 Job role—function; Ranking/Importance    -   3. Yrs exp—specific; Ranking/Importance Duration score primarily        depends on the number of months of work experience        -   a. Each position has an optimal duration        -   b. For example if a candidate remains in a Junior post for a            long time the duration/experience score will be lower        -   C. S_(Duration)=Duration till Duration<OptimalDuration for            that post or else            S_(Duration)=Duration−W*(Duration/OptimalDuration) where W            is a weight fixed by algorithm    -   4. Network strength—How wide and deep the candidates network was        within the past company.

Company Score—for Each Experience a Company Score is Calculated

-   -   1. Company score is primarily based on Revenue, Profits, Number        of employees, Other Rankings (Fortune 500)    -   2. S_(Company)=W₁*Revenue+W₂*Profits+W₃*Employees+W₄*(1/Rank)        where W stands for weightage

Skills/Competencies:

-   -   1. Functional        -   a. Skill: specific skill rating (derived from system);            ranking/importance        -   b. Competency: specific rating (derived from system);

ranking/importance

-   -   2. Soft/Behavioral Skills        -   a. Skill: specific skill rating (derived from system);            ranking/importance        -   b. Competency: specific rating (derived from system);            ranking/importance

Location:

-   -   1. Experience score also depends on the company work location    -   2. For example working at a company headquarters/main branch        adds more to the experience    -   3. Location score varies from company to company and is        determined by the database of company to locations mapping    -   4. Along with this preference is also accounted for        Similar to Other Company Employees Factor (Include or        not-Specified by Recruiter Along with Weight)

The method includes determining a career path of at least one employeeof a company and at least one candidate from the plurality ofcandidates; and comparing career paths of the at least one employee andthe at least one candidate to assist the recruiter in selection of theat least one candidate for the at least one job.

Companyemployeessimilaritiesscore currentnetworkmembersincompany (pulledfrom social network)+peoplewithsimilarbackgroundeurrentlyincompany(coliege name, similar degree, previous same company)

Peoplewithsimilarbackgroundcurrentlyincompany—derived from data providedby company or college about current employee base (can also be derivedfrom linkedin/socialnetwork data)

The CandidateScore is a dynamic score based on recruiter specifiedcriteria, that is then benchmarked. Priorities are used as weights tocompute a weighted average CandidateScore based on the parameters therecruiter cares about.

While there is no limit on number of parameters, needless to say weightsbecome of limited importance if there are too many parameters/criteriaselected by the recruiter.

CandidateScore=weighteducation*educationscore+weightjobrole*jobrolescore+weightcompanyscore+weightskills*skillscore+locationweight*locationscore+weightsimilarityscore*companyemployeessimilaritiesscore

Educationscore=matchwitheducationparametershigh+matcheducationparametersmedium+matchwitheducationparamterslow

High, medium and low weights have a derived weighted average scoreadding to 100%; High is always 60% of total, medium 30% and Low is 10%of score in all cases.

The recruiter also specifies relative importance of education, andskills, and job roles and relative weightage is used to assign weightsin formula of CandidateScore where all weights add up to 100% andsimilar high, medium, and low weights are assigned.

The method includes determining a career path of at least one employeeof a company and at least one candidate from the plurality ofcandidates; and comparing career paths of the at least one employee andthe at least one candidate to assist the recruiter in selection of theat least one candidate for the at least one job.

Social/Personality Inputs:

In addition a recruiter can choose to include assessment ofpersonality/behavior types along with analytics derived from socialpresence on social networks to determine the fit of the candidate withrespect to the role and company.

Candidate Cost to Company

In addition the recruiter can leverage candidate's salary profile, oranalytics provided on what the candidate would cost to the company basedon their market value as a criteria in the fit. The recruiter couldprovide a range of cost they are willing to bear and candidates closerto this cost would be receiving a higher score.

Other Inputs to Algorithm are:

-   -   1. Global/Local—is the candidate working in a single country    -   2. Stationary/Shifting—is the candidate changing between        companies frequently    -   3. Show overqualified candidates—Consider candidates who worked        as a

Senior for Junior post

The recruiter may choose to include or exclude these elements fromalgorithm. The algorithm is dynamic allowing user to select whichparameters matter.

In addition the recruiter can simply load a job description and getscores across candidates.

Once scoring of all candidates is done, the recruiter is presented withlist of candidates and corresponding CandidateScore sorted according toscore.

Higher candidate score implies higher likelihood of candidate gettingselected and succeeding within the company.

Analytics Engine Components

As illustrated in FIG. 0, the analytics engine has the followingcomponents:

-   -   1. Database of data parsed from resumes/social network profiles        put into structured format    -   2. Algorithm tables    -   3. Data sets supporting algorithms    -   4. Samples database    -   5. Benchmarking database    -   6. Machine Learning Elements (FIG. 3)

The system is designed to be machine learning so that every new userprofile that comes into the system improves all data sets, benchmarking,algorithms, etc. Components of machine learning are discussed inrelevant sections.

The database of user profiles includes the following information

FirstName LastName Email PhoneNumber Address Education(Array ) =>0(Array) => Degree Field SchoolName LocationCity LocationCountryLocationString Duration(In Months) Grade Awards(Array) => Award0 Award1Award2 Organizations(Arra Organizations y) => 0 Organizations 1Organizations 2 Bullets(Array) => BulletText0 BulletText1 BulletText2Experience(Arra y) => 0(Array) => Position Function CompanyName(currently as Industry null) LocationCountry LocationString Duration(InMonths) Skills(Array) => 0(Array) => SkillID HumanName MachineNameSkillType{Soft,Har d} 1(Array) => SkillID HumanName MachineNameSkillType{Soft,Har d} 2(Array) => SkillID HumanName MachineNameSkillType{Soft,Har d} Bullets(Array) => BulletText0 BulletText1BulletText2 Interests(Array) Additional => Interest0 Interest1 Interest2Languages(Arra y) => Language0 Language1 Language2 Bullets(Array) =>BulletText0 BulletText1 BulletText2

Algorithm tables store the algorithms and formulas for each module asoutlined in each section. Outputs of calculations are stored inbenchmarking tables described in [0125]

The following data sets support the execution of all algorithms.

-   -   1. Resume/CV section repository    -   2. Action-oriented verbs database along with weights (higher for        stronger action verbs, lower for weak action verbs, along with        permutations of actions in tenses)    -   3. Avoided phrases repository    -   4. Passive language repository    -   5. Degree harmonizing database    -   6. Degrees to skills mapping database    -   7. Skills database—listing of over 10,000 skills with        corresponding keywords, phrases, patterns that showcase the        skills. Skills mapping to competencies.    -   8. Job role mapping database—job roles mapped to functions,        industries with corresponding skills needed in the role, along        with weights for each skill by relative importance.    -   9. Job role harmonization database    -   10. Harmonization of colleges across tiers    -   11. Harmonization of job roles for similarity, harmonization of        functions for similarity

The samples suggestions database contains the following:

-   -   1. Bullet samples tagged to industry, function, job role,        skills, competencies, years of experience, education, type of        experience (e.g. awards, extracurricular, etc)    -   2. Samples for resumes for different types of careers    -   3. Samples for different sections of a CV/resume e.g. objective,        summary, skills sections, etc    -   4. Flags to suggest which stage of development they are (e.g.        approved—ready for consumption by user, edit—currently being        edited by writer)

The benchmarking database contains for each user their scores for allportions of algorithms including Resume Score, CareerFitScore,

The database is structured such that each users attributes along withscores for each element are stored. Every new user adds to this databasedynamically and this database is used to provide customized benchmarkingto all users.

Benchmarking can be customized on any attribute of a user including,past college, tier of college, job role, company, years of experience,skills, competencies to show relative positioning with respect to eachor a combination of these elements.

Modifications to embodiments of the present invention described in theforegoing are possible without departing from the scope of the presentinvention as defined by the accompanying claims. Expressions such as“including”, “comprising”, “incorporating”, “have”, “is” used todescribe and claim the present invention are intended to be construed ina non-exclusive manner, namely allowing for items, components orelements not explicitly described also to be present. Reference to thesingular is also to be construed to relate to the plural.

What is claimed is:
 1. A computer-implemented method comprising:receiving a document that represents a career profile of a candidateusing text arranged in accordance with a presentation structure; parsingthe document to identify a parameter value for a parameter of the careerprofile, wherein the parameter value is indicative of an extent to whichthe document exhibits the parameter; determining other candidates thatare similar to the candidate based on a profile vector representing oneor more attributes associated with the career profile of the candidateand a plurality of profile vectors associated with the other candidates,wherein the profile vector and the plurality of profile vectors aregenerated by a machine learning model that has been trained to generateprofile vectors based on training data comprising a plurality of careerprofiles associated with a plurality of candidates; determining acategory score based on the parameter value and a benchmark rule,wherein the benchmark rule is based on respective parameter values ofthe other candidates that are similar to the candidate, and wherein thecategory score corresponds to a category that comprises the parameter;providing, by way of a user interface, feedback on the career profile inaccordance with the category score, wherein the feedback comprises (i) asuggested modification to the document and (ii) a plurality ofcandidate-specific samples for executing the suggested modification,wherein each respective candidate-specific sample of the plurality ofcandidate-specific samples is determined to increase the category scorewhen added into the document; and based on providing the feedback,receiving, by way of the user interface, a selection of at least onesample of the plurality of candidate-specific samples for addition intothe document.
 2. The computer-implemented method of claim 1, whereindetermining the category score comprises: determining, based on thebenchmark rule, a parameter weight for the parameter value; and applyingthe parameter weight to the parameter value.
 3. The computer-implementedmethod of claim 1, wherein the category comprises one or more of apresentation category, an impact category, or a skill category, wherein,when the category is the presentation category, the parameter value isindicative of one or more of a margin of the document, a section layoutof the document, a formatting of the document, or a spelling of thedocument, wherein, when the category is the impact category, theparameter value is indicative of a career progression of the candidate,a bullet structure of the document, or an achievement level of thecandidate, and wherein, when the category is the skill category, theparameter value is indicative of a soft skill of the candidate, afunctional skill of the candidate, or an industry skill of thecandidate.
 4. The computer-implemented method of claim 1, furthercomprising: selecting a category weight corresponding to the categorybased on the benchmark rule; and determining an overall score of thecareer profile by applying the category weight to the category score. 5.The computer-implemented method of claim 1, wherein the parameter valueis selected from a normalized range that extends from a minimum value toa maximum value, wherein the minimum value indicates that the parameteris absent from the career profile, and wherein the maximum valueindicates that the parameter is maximally represented in the careerprofile.
 6. The computer-implemented method of claim 1, whereinreceiving the document comprises: receiving the document as an upload toa career analytics platform by way of a user interface provided by thecareer analytics platform; receiving the document from a database hostedoutside of the career analytics platform; or generating the documentusing the career analytics platform.
 7. The computer-implemented methodof claim 1, further comprising: identifying, based on the parametervalue, a classification of the candidate, wherein the benchmark rule isselected based on the classification of the candidate.
 8. Thecomputer-implemented method of claim 1, further comprising: generating abenchmarking curve based on a peer group of the candidate; and plottinga representation of the career profile of the candidate on thebenchmarking curve based on the category score.
 9. Thecomputer-implemented method of claim 1, wherein the suggestedmodification indicates to remove a gap in a career of the candidate,wherein the plurality of candidate-specific samples are selected toremove the gap, and wherein at least one candidate-specific sample ofthe plurality of candidate-specific samples corresponds to the parameterand the category.
 10. The computer-implemented method of claim 1,wherein the plurality of candidate-specific samples are selected for thecareer profile of the candidate based on one or more tags associatedwith the plurality of candidate-specific samples.
 11. Thecomputer-implemented method of claim 1, further comprising: indicating afirst section of the document using a first color corresponding to afirst type of feedback provided for the first section; and indicating asecond section of the document using a second color corresponding to asecond type of feedback provided for the second section, wherein thefirst type of feedback is different from the second type of feedback,and wherein the first color is different from the second color.
 12. Thecomputer-implemented method of claim 1, wherein the user interface isconfigured to provide for editing of the document to increase thecategory score.
 13. The computer-implemented method of claim 1, furthercomprising: identifying a cluster of career profiles within a databaseof a career analytics platform, wherein the cluster indicates at leastone career path for the candidate; determining that a skill of thecandidate is present within the cluster; applying the benchmark rule todetermine a skill score of the skill of the candidate; and determining ajob fit score for the candidate based on the skill score.
 14. Thecomputer-implemented method of claim 13, further comprising: identifyinga job opportunity for the candidate based on the job fit score;identifying, based on a network score, a professional from a network ofthe candidate for the job opportunity, wherein the network score isdetermined based on attributes of one or more members of the network ofthe candidate; and determining a job fit at company score for the jobopportunity based on the job fit score and the network score.
 15. Thecomputer-implemented method of claim 1, further comprising: receiving,from the candidate, a selection of a career path to be achieved within atimeframe; and recommending to the candidate an action to take to pursuethe career path.
 16. The computer-implemented method of claim 1, furthercomprising: receiving a second career profile of the candidate;determining an extent of synchronization between the career profile andthe second career profile; and generating an indication of the extent ofsynchronization.
 17. The computer-implemented method of claim 1, furthercomprising: receiving, from a recruiter, a job description and at leastone parameter corresponding to the job description; accessing aplurality of career profiles of a plurality of candidates; anddetermining, for each of the plurality of career profiles, acorresponding job fit score based on the job description and the atleast one parameter.
 18. The computer-implemented method of claim 17,further comprising: determining a first career path of an employee of acompany and second career path of a candidate from the plurality ofcandidates; comparing the first career path to the second career path;and generating a result of comparing the first career path to the secondcareer path to assist the recruiter in selecting at least one candidatefor a position represented by the job description.
 19. A systemcomprising: a processor; and a non-transitory computer-readable storagemedium having stored thereon instructions that, when executed by theprocessor, cause the processor to perform operations comprising:receiving a document that represents a career profile of a candidateusing text arranged in accordance with a presentation structure; parsingthe document to identify a parameter value for a parameter of the careerprofile, wherein the parameter value is indicative of an extent to whichthe document exhibits the parameter; determining other candidates thatare similar to the candidate based on a profile vector representing oneor more attributes associated with the career profile of the candidateand a plurality of profile vectors associated with the other candidates,wherein the profile vector and the plurality of profile vectors aregenerated by a machine learning model that has been trained to generateprofile vectors based on training data comprising a plurality of careerprofiles associated with a plurality of candidates; determining acategory score based on the parameter value and a benchmark rule,wherein the benchmark rule is based on respective parameter values ofthe other candidates that are similar to the candidate, and wherein thecategory score corresponds to a category that comprises the parameter;providing, by way of a user interface, feedback on the career profile inaccordance with the category score, wherein the feedback comprises (i) asuggested modification to the document and (ii) a plurality ofcandidate-specific samples for executing the suggested modification,wherein each respective candidate-specific sample of the plurality ofcandidate-specific samples is determined to increase the category scorewhen added into the document; and based on providing the feedback,receiving, by way of the user interface, a selection of at least onesample of the plurality of candidate-specific samples for addition intothe document.
 20. A non-transitory computer-readable storage mediumhaving stored thereon instructions that, when executed by a computingdevice, cause the computing device to perform operations comprising:receiving a document that represents a career profile of a candidateusing text arranged in accordance with a presentation structure; parsingthe document to identify a parameter value for a parameter of the careerprofile, wherein the parameter value is indicative of an extent to whichthe document exhibits the parameter; determining other candidates thatare similar to the candidate based on a profile vector representing oneor more attributes associated with the career profile of the candidateand a plurality of profile vectors associated with the other candidates,wherein the profile vector and the plurality of profile vectors aregenerated by a machine learning model that has been trained to generateprofile vectors based on training data comprising a plurality of careerprofiles associated with a plurality of candidates; determining acategory score based on the parameter value and a benchmark rule,wherein the benchmark rule is based on respective parameter values ofthe other candidates that are similar to the candidate, and wherein thecategory score corresponds to a category that comprises the parameter;providing, by way of a user interface, feedback on the career profile inaccordance with the category score, wherein the feedback comprises (i) asuggested modification to the document and (ii) a plurality ofcandidate-specific samples for executing the suggested modification,wherein each respective candidate-specific sample of the plurality ofcandidate-specific samples is determined to increase the category scorewhen added into the document; and based on providing the feedback,receiving, by way of the user interface, a selection of at least onesample of the plurality of candidate-specific samples for addition intothe document.